Description

LAB – 1
Deepak Yadav
Part I
Figure given below shows a simple example composed of three state and two actions

ans) #code
from typing import Tuple
class Environment:
def __init__(self):
self._initial_state=1 self._allowed_action=[0,1] self._states=[1,2,3] self._current_state=self._initial_state
def step(self,action:int)->Tuple[int,int]:
if action not in self._allowed_action:
raise ValueError(“Action is not allowed”)
reward=0 if action==0 and self._current_state==1:
self._current_state=2 reward=1
return (self._current_state,reward)
elif action==1 and self._current_state==1:
self._current_state=3 reward=10
return (self._current_state,reward)
elif action==0 and self._current_state==2:
self._current_state=1 reward=0
return (self._current_state,reward)
elif action==1 and self._current_state==2:
self._current_state=3 reward=1
return (self._current_state,reward)
elif action==0 and self._current_state==3:
self._current_state=2 reward=0
return (self._current_state,reward)
elif action==1 and self._current_state==3:
self._current_state=3 reward=10
return (self._current_state,reward)
def reset(self)->int:
self._current_state=self._initial_state return self._current_state
env=Environment() state=env.reset()
actions=[0,0,1,1,0,1]
print(f”Intial state is {state}”)
for action in actions:
next_state, reward=env.step(action) print(f”From state {state} to state {next_state} with action {action}, reward: {reward}”) state=next_state
#Output

Part II
Creating a complex structure code with 5 states and 3 actions ans) #code
from typing import Tuple
class Environment:
def __init__(self):
self._initial_state=1 self._allowed_action=[0,1,2] self._states=[1,2,3,4,5] self._current_state=self._initial_state
def step(self,action:int)->Tuple[int,int]:
if action not in self._allowed_action:
raise ValueError(“Action is not allowed”)
reward=0 if action==0 and self._current_state==1:
self._current_state=2 reward=1
return (self._current_state,reward)
elif action==1 and self._current_state==1:
self._current_state=3 reward=1
return (self._current_state,reward)
elif action==2 and self._current_state==1:
self._current_state=1 reward=0
return (self._current_state,reward)
elif action==0 and self._current_state==2:
self._current_state=1 reward=0
return (self._current_state,reward)
elif action==1 and self._current_state==2:
self._current_state=3 reward=1
return (self._current_state,reward)
elif action==2 and self._current_state==2:
self._current_state=2 reward=0
return (self._current_state,reward)
elif action==0 and self._current_state==3:
self._current_state=2 reward=0
return (self._current_state,reward)
elif action==1 and self._current_state==3:
self._current_state=4 reward=1
return (self._current_state,reward)
elif action==2 and self._current_state==3:
self._current_state=3 reward=0
return (self._current_state,reward)
elif action==0 and self._current_state==4:
self._current_state=3 reward=0
return (self._current_state,reward)
elif action==1 and self._current_state==4:
self._current_state=5
reward=10
return (self._current_state,reward)
elif action==2 and self._current_state==4:
self._current_state=4 reward=0
return (self._current_state,reward)
elif action==0 and self._current_state==5:
self._current_state=4 reward=0
return (self._current_state,reward)
elif action==1 and self._current_state==5:
self._current_state=5 reward=10
return (self._current_state,reward)
elif action==2 and self._current_state==5:
self._current_state=3 reward=0
return (self._current_state,reward)
def reset(self)->int:
self._current_state=self._initial_state return self._current_state
env=Environment() state=env.reset()
actions=[0,2,0,1,2,1,2,0,1,2,1]
print(f”Intial state is {state}”)
for action in actions:
next_state, reward=env.step(action) print(f”From state {state} to state {next_state} with action {action}, reward: {reward}”) state=next_state
#Output

Part III
Generalizing the problem with n state and m actions ans)
Performing the same model demonstration as the first one
#code
from typing import Tuple
class Environment:
def __init__(self,_allowed_action,_states):
self.trf = {} self._initial_state=1 self._allowed_action=_allowed_action self._states=_states self._current_state=self._initial_state print(“Enter the transition”) print(“Format : current_state action next_state reward”) for i in range((len(_allowed_action)*len(_states))):
new=input().split(‘ ‘) new=[int(x) for x in new] s, a, n, r = new self.trf[s,a]=(n, r)
def step(self,action:int)->Tuple[int,int]:
perform = self.trf[(self._current_state,action)] #matching the dictonary to perform
action
if perform:
n,r=perform self._current_state=n return (self._current_state,r)
else:
print(“Error!!!”) return 0
def reset(self)->int:
self._current_state=self._initial_state return self._current_state
st=input(“Enter the states : “).split() st=[int(x) for x in st] ac=input(“Enter the actions : “).split() ac=[int(x) for x in ac] env=Environment(st,ac)
state=env.reset()
act=input(“Enter the actions to be performed : “).split() act=[int(x) for x in act] actions=act
print(f”Intial state is {state}”)
for action in actions:
next_state, reward=env.step(action) print(f”From state {state} to state {next_state} with action {action}, reward: {reward}”) state=next_state
#Output